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<p><b>Abstract</b>—This correspondence introduces the <it>weighted-Parzen-window</it> classifier. The proposed technique uses a clustering procedure to find a set of reference vectors and weights which are used to approximate the <it>Parzen-window</it> (<it>kernel-estimator</it>) classifier. The weighted-Parzen-window classifier requires less computation and storage than the full Parzen-window classifier. Experimental results showed that significant savings could be achieved with only minimal, if any, error rate degradation for synthetic and real data sets.</p>
Nonparametric classifiers, Parzen-windows, kernel estimator, clustering, training samples, discriminant analysis, Bayes error, leave-one-out, holdout.

O. I. Camps and G. A. Babich, "Weighted Parzen Windows for Pattern Classification," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 18, no. , pp. 567-570, 1996.
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